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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q28-Q33):

NEW QUESTION # 28
A company is planning to use Amazon SageMaker to make classification ratings that are based on images.
The company has 6 ## of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?

A. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
B. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
C. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
D. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.

Answer: D

Explanation:
Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.

NEW QUESTION # 29
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?

A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to
0.0.0.0/0.
B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account.Set up interface VPC endpoints for SageMaker and Amazon Redshift.
D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.

Answer: C

Explanation:
S3 Gateway Endpoint: Allows private access to S3 from within a VPC without requiring a public IPv4 address, ensuring that data transfer between the primary and secondary accounts is secure and private.
Bucket Policy Update: The S3 bucket policy in the secondary account must explicitly allow access from the primary account's IAM principals to provide the necessary permissions.
Interface VPC Endpoints: Required for private communication between the VPC and Amazon SageMaker and Amazon Redshift services, ensuring the solution operates without public internet access.
This configuration meets the requirement to avoid public IPv4 addresses and allows secure and private communication between the accounts.

NEW QUESTION # 30
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.
Which solution will meet these requirements?

A. Run an Amazon EMR job to change the sensitive data to random values.
B. Prepare the data by using AWS Glue DataBrew.
C. Use Amazon Made to categorize the sensitive data.
D. Run an AWS Batch job to change the sensitive data to random values.

Answer: B

Explanation:
AWS Glue DataBrew provides an easy-to-use interface for preparing and transforming data, including masking or obfuscating sensitive information. It offers built-in data masking features, allowing the ML engineer to handle sensitive data securely while retaining its structure and meaning. This solution is efficient and requires minimal coding, making it ideal for ensuring sensitive data is masked before model building begins.

NEW QUESTION # 31
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

A. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
B. Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
C. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
D. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.

Answer: C

NEW QUESTION # 32
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

A. Configure the competitor's name as a blocked phrase in Amazon Q Business.
B. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
C. Configure an Amazon Q Business retriever to exclude the competitor's name.
D. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.

Answer: A

Explanation:
Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

NEW QUESTION # 33
......

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